Enactment of the Check Clearing for the 21st Century Act (“Check 21”) has enabled banks to deploy various digital schemes to process check deposits, including distributed image capture, image exchange, Remote Deposit Capture (“RDC”), and check transaction conversion to other payment types such as Automated Clearing House (“ACH”) transfers. In the RDC approach, for example, a banking customer captures an image of each side of a check and transmits them, along with other information typically stored as metadata, to the customer's bank. RDC can be implemented on various technology platforms. Today, it is widely used on smart phones using mobile applications offered by many banks operating in the United States.
In another implementation, a customer inserts a check into a receiving slot at an ATM, wherein the ATM's scanner captures the check as an image, and may further analyze the image using intelligent recognition technology, such as optical character recognition software (“OCR”). The OCR software allows the ATM to ascertain the values of some fields appearing on the check, for example, by recognizing characters and values in the check's E-13B code line, and to store the recognized values as metadata associated with the transaction.
Although intended in part to streamline and automate the check deposit and clearing process, existing digital check processing schemes have increased significantly the need to detect the processing of a single transaction multiple times. For example, where a customer deposits a check using RDC on a smart phone, the customer may deposit the same check at a later time using an ATM. Whether this is done unintentionally or with fraudulent intent, the paying bank and the payee bank both must ensure that a single check is processed as only one transaction, even where there have been multiple deposit attempts of that check.
In a reverse example, where a customer regularly deposits payroll checks bearing substantially the same information, existing automated systems are more likely to arrive at a false positive determination, i.e., identify the distinct checks as duplicates, and mistakenly treat them as one transaction. This is partly because these automated schemes do not monitor or capture every piece of information on a check, since doing so would require significant additional time and computing resources, and would thereby increase transaction costs. Given the volume of transactions in the check processing industry at any given time, these costs can be prohibitive. An unintended consequence of this resource-saving approach, i.e., monitoring less than all of the information available on a check image, is that it leads to two or more distinct check transactions appearing to be duplicates; differences appearing in non-monitored portions of the checks may go unnoticed. In the payroll checks example above, each payroll check may be identical to others deposited by a single customer except for the date field, which typically is not checked at the time of deposit. In light of the substantial potential for problems, the paying bank and the payee bank both must ensure that each distinct transaction is processed, even where the transactions appears to be duplicates.
In both examples, where images of the same check appear to be distinct, and where images of two or more checks appear to be duplicates, there is an increased need for a second level review, in the form of a further automated process, human monitoring, or both. For example, the only currently available automated solution for a second level review is to use OCR technology to find and read fields on a check image (fields that were not found or read in the first level review), and to use this additional information to determine whether two or more check images in question represent the same underlying transaction. Alternatively, or in addition to the automated review, the information may be forwarded to a review operator who must, as a second level reviewer, interrogate the information associated with each transaction suspected of being a duplicate of another transaction. This forwarded information may include check images and their associated metadata. Whether the second level review is automated using character recognition software, or performed by a human reviewer, or both, the per-transaction time and cost of the automated system increase. Additionally, in the case of OCR, the OCR process is not uniform. For example, OCR software evaluates the handwriting portion of a check differently than the check's pre-printed portions. Adding to the difficulty is the lack of uniformity in many of the properties of a check, including often difficult to predict variations, such as character strokes and placement of handwritten text on a check.
The shortcomings of current solutions may lead to a bank refusing to process a check transaction based on an erroneous determination that it is a duplicate transaction; or the bank may process a single transaction multiple times based on a failure to determine that one transaction is a duplicate of another. In either case, customer satisfaction and the reputation of the bank decline. Furthermore, the bank may lose business.
Various factors contribute to the limitations of current automated systems, including differences in checks formats. For example, check size, serialization (or lack thereof), non-standard features (e.g. placement of address block; personalized graphics), differences in handwriting styles or the writing instrument used, illegible handwriting, or handwriting placement (e.g. writing indicating check amount may run outside of designated box), all can make it difficult for an automated process to streamline check image analysis.
Another factor that contributes to the limitations of existing automated systems is differences in the devices used to capture check images and the physical environments in which such devices are deployed. Characteristics of a digitally captured image affected by the particular device that captures and/or stores that image, and the physical environment in which the image is captured, include, without limitation, image type, size, compression, color, resolution, focus, and noise. Factors responsible for these differences in characteristics include, without limitation, differences in device configuration (including software), image processing (e.g. changing exposure, contrast, or other parameters), computing resources, lighting used by a scanner or other capture device, and dust particles on the item to be scanned and on the capture device.
For example, the same check may yield different images when captured using an ATM's scanner compared to a smart phone's camera. Although the images may look substantially similar to the human eye, the pel data that constitute the images are sufficiently different that a strictly pel-by-pel comparison of the two will result in a finding of dissimilarity. Furthermore, other existing comparison methods are not sophisticated enough to accurately and reliably perform more intelligent comparisons with sufficient speed or sufficiently low processing power to make them worthwhile to implement.
A desirable solution to these challenges increases the performance of automated check processing systems by reducing the growing costs associated with reliably comparing the check images that these systems use, and by decreasing the need to for a second level review that uses intelligent character recognition, human monitoring, or both.